


'''simple docstring'''
import math




class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							,      a_							,      a_							)    ->  int:
					lowercase						:		Tuple   					=		0.0
					lowercase						:		int   					=		0.0
					for i in range(len(_lowercase							)							):
						da += math.pow((sample[i] - weights[0][i])							,      2							)
						da += math.pow((sample[i] - weights[1][i])							,      2							)
						return 0 if da > da else 1
					return 0


				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							)    ->  list[list[int | float]]:
					for i in range(len(_lowercase							)							):
						weights[j][i] += alpha * (sample[i] - weights[j][i])
					return weights




def 	_A  (       )  ->				Optional[Any]:
	lowercase						:		Optional[int]   					=		[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

	# weight initialization ( n, C )
	lowercase						:		Optional[int]   					=		[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

	# training
	lowercase						:		Tuple   					=		SelfOrganizingMap()
	lowercase						:		str   					=		3
	lowercase						:		Union[str, Any]   					=		0.5

	for _ in range(a__							):
		for j in range(len(a__							)							):
			# training sample
			lowercase						:		Optional[int]   					=		training_samples[j]

			# Compute the winning vector
			lowercase						:		str   					=		self_organizing_map.get_winner(a__							,a__							)

			# Update the winning vector
			lowercase						:		str   					=		self_organizing_map.update(a__							,a__							,a__							,a__							)

    # classify test sample
	lowercase						:		int   					=		[0, 0, 0, 1]
	lowercase						:		str   					=		self_organizing_map.get_winner(a__							,a__							)

	# results
	print(F'''Clusters that the test sample belongs to : {winner}'''							)
	print(F'''Weights that have been trained : {weights}'''							)


# running the main() function
if __name__ == "__main__":
					main()





from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


